APF-Based Car Following Behavior Considering Lateral Distance

Yang, Zhaosheng; Yao, Yu; Yu, Duk Jun; Zhou, Huxing; Mo, Xianglun

doi:10.1155/2013/207104

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…This system estimates driving risk by assuming that a virtual spring is attached between the vehicle and pedestrian. Zhao-Sheng Yang et al [20] established a car following model based on the artificial potential field theory, where the host vehicle is treated like an individual charge in a potential field. In this way, the influences of traffic factors on vehicles can be simplified as interactive attractive and repulsive forces between vehicles, which allow car following behaviors, with consideration for lateral distance, to be researched from a new perspective.…”

Section: Introductionmentioning

confidence: 99%

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Wang

2015

IEEE Trans. Intell. Transport. Syst.

267

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Vehicle driving safety is influenced by many factors, including drivers, vehicles, and road environments. The interactions among them are quite complex. Consequently, existing methods that evaluate driving safety perform inadequately because they only consider limited factors and their interactions. As such, it is difficult for kinematics-based and dynamics-based vehicle driving safety assistant systems to adapt to increasingly complex traffic environments. In this paper, we propose a new concept, i.e., the driving safety field. The concept makes use of field theory to represent risk factors owing to drivers, vehicles, road conditions, and other traffic factors. A unified model of the driving safety field is constructed, which includes the following three parts: 1) a potential field, which is determined by nonmoving objects on the roads, such as a stopped vehicle; 2) a kinetic field, which is determined by the moving objects on roads, such as vehicles and pedestrians; and 3) a behavior field, which is determined by the individual characteristics of drivers. Moreover, the applications of the model are proposed, and its application to a typical carfollowing scenario is illustrated, which evaluates the risks caused by multiple traffic factors. The driving safety field can reveal driver-vehicle-road interactions and their influences on driving safety, as well as predict driving safety trends owing to dynamic changes. In addition, the model can provide a new foundation for establishing driving safety measures and active vehicle control under complex traffic environments.

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Section: Introductionmentioning

confidence: 99%

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Wang

2015

IEEE Trans. Intell. Transport. Syst.

267

View full text Add to dashboard Cite

show abstract

“…This system estimates driving risk by assuming that a virtual spring is attached between the vehicle and a pedestrian. Zhao-Sheng Yang et al [17] established a novel car following model based on Artificial Potential Field theory and the host vehicle is just like an individual charge in the potential field. In this way, the influences of traffic factors on the vehicles can be simplified as the interactive attractive forces and repulsive forces between vehicles, and then car following behaviors considering lateral distance can be researched in a new perspective.…”

Section: Introductionmentioning

confidence: 99%

The concept and modeling of driving safety field based on driver-vehicle-road interactions

Wang

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Vehicle driving safety is influenced by many factors including drivers, vehicles, and roads, the interactions among which are quite complex. Consequently, existing methods that evaluate driving safety perform inadequately since they consider only limited factors and their effects. As such, it is difficult for vehicle kinematics-and dynamics-based driving safety assistant systems to adapt to increasingly complex traffic environments. In this study, we propose a new concept, driving safety field. The concept makes use of field theory to represent the risks of driving caused by drivers, vehicles, roads, and other traffic factors. A unified model of the driving safety field is constructed, which includes the following three parts: i) a potential field, which is determined by no-moving objects on the roads, such as a stopped vehicle; ii) a kinetic field, which is determined by the moving objects on roads, such as vehicles and pedestrians; and iii) a behavior field, which is determined by the driver's individual characteristics. Moreover, the model's application in a typical car-following scenario is illustrated, which evaluates the risks caused by traffic factors. The driving safety field can reveal driver-vehicle-road interactions and their influences on driving safety in order to predict trends of dynamic changes in driving safety. In addition, it can provide a new platform for driving safety measures and active vehicle control under complex traffic environments.

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“…In traffic network, the distributed system is often used in the fields of path searching, traffic signal control, and so on, such as solving TSP problem [6,7], vehicle navigation, and intersection signal control. Guo and Huang [8] proposed an optimal control method with feedback adjustment.…”

Section: Dynamic Traffic Assignment Theory: a Brief Reviewmentioning

confidence: 99%

Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers

Liu

Yao

2015

Discrete Dynamics in Nature and Society

Self Cite

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In order to respond to the variable state of traffic network in time, a distributed dynamic traffic assignment strategy is proposed which can improve the intelligent traffic management. The proposed dynamic assignment method is based on utility theory and is oriented to different levels of induced users. A distributed model based on the marginal utility is developed which combines the advantages of both decentralized paradigm and traveler preference, so as to provide efficient and robust dynamic traffic assignment solutions under uncertain network conditions. Then, the solution algorithm including subroute update and subroute calculation is proposed. To testify the effectiveness of the proposed model in optimizing traffic network operation and minimizing traveler’s cost on different induced levels, a sequence numerical experiment is conducted. In the experiment, there are two test environments: one is in different network load conditions and the other is in different deployment coverage of local agents. The numerical results show that the proposed model not only can improve the running efficiency of road network but also can significantly decrease the average travel time.

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APF-Based Car Following Behavior Considering Lateral Distance

Cited by 18 publications

References 15 publications

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

The concept and modeling of driving safety field based on driver-vehicle-road interactions

Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers

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